Seasonal change and microhabitat association of Arctic spider assemblages (Arachnida: Araneae) on Victoria Island (Nunavut, Canada)
Bibliographic record
Abstract
Abstract Arctic ecosystems are characterised by a mosaic of distinct microhabitats, which play a key role in structuring biodiversity. Understanding species diversity in relation to these microhabitats, and how communities are structured seasonally, is imperative to properly conserve, monitor, and manage northern biodiversity. Spiders (Arachnida: Araneae) are dominant arthropod predators in the Arctic, yet the seasonal change in their communities in relation to microhabitat variation is relatively unknown. This research quantified how spider assemblages are structured seasonally and by microhabitat, near Cambridge Bay, Nunavut, Canada. In 2014, spiders were collected in 240 pan and pitfall traps placed in common microhabitat types (two wet and two dry) from 3 July to 11 August, the active season in the high Arctic. In total, 10 353 spiders from 22 species and four families were collected. Non-metric multidimensional scaling ordinations revealed that spider assemblages from wet habitats were distinct from those occurring in drier habitats, but that differences within each of those habitats were not evident. Abundance and diversity was highest in wet habitats and differed significantly from dry habitats; both these variables decreased seasonally. Spider assemblages in the north are structured strongly along moisture gradients, and such data informs planning for future ecological monitoring in the Arctic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".